Search Results

Search found 2467 results on 99 pages for 'pinal dave'.

Page 12/99 | < Previous Page | 8 9 10 11 12 13 14 15 16 17 18 19  | Next Page >

  • SQL SERVER – Solution – Puzzle – Statistics are not Updated but are Created Once

    - by pinaldave
    Earlier I asked puzzle why statistics are not updated. Read the complete details over here: Statistics are not Updated but are Created Once In the question I have demonstrated even though statistics should have been updated after lots of insert in the table are not updated.(Read the details SQL SERVER – When are Statistics Updated – What triggers Statistics to Update) In this example I have created following situation: Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Auto Update Statistics and Auto Create Statistics for database is TRUE Now I have requested two things in the example 1) Why this is happening? 2) How to fix this issue? I have many answers – here is the how I fixed it which has resolved the issue for me. NOTE: There are multiple answers to this problem and I will do my best to list all. Solution: Create nonclustered Index on column City Here is the working example for the same. Let us understand this script and there is added explanation at the end. -- Execution Plans Difference -- Estimated Execution Plan Vs Actual Execution Plan -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO CREATE NONCLUSTERED INDEX IX_ExecTable1 ON ExecTable (City); GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -------------------------------------------------------------- -- Round 2 -- Insert One Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -- Clean up Database DROP TABLE ExecTable GO When I created non clustered index on the column city, it also created statistics on the same column with same name as index. When we populate the data in the column the index is update – resulting execution plan to be invalided – this leads to the statistics to be updated in next execution of SELECT. This behavior does not happen on Heap or column where index is auto created. If you explicitly update the index, often you can see the statistics are updated as well. You can see this is for sure happening if you follow the tell of John Sansom. John Sansom‘s suggestion: That was fun! Although the column statistics are invalidated by the time the second select statement is executed, the query is not compiled/recompiled but instead the existing query plan is reused. It is the “next” compiled query against the column statistics that will see that they are out of date and will then in turn instantiate the action of updating statistics. You can see this in action by forcing the second statement to recompile. SELECT FirstName, LastName, City FROM ExecTable WHERE City = ‘New York’ option(RECOMPILE) GO Kevin Cross also have another suggestion: I agree with John. It is reusing the Execution Plan. Aside from OPTION(RECOMPILE), clearing the Execution Plan Cache before the subsequent tests will also work. i.e., run this before round 2: ————————————————————– – Clear execution plan cache before next test DBCC FREEPROCCACHE WITH NO_INFOMSGS; ————————————————————– Nice puzzle! Kevin As this was puzzle John and Kevin both got the correct answer, there was no condition for answer to be part of best practices. I know John and he is finest DBA around – his tremendous knowledge has always impressed me. John and Kevin both will agree that clearing cache either using DBCC FREEPROCCACHE and recompiling each query every time is for sure not good advice on production server. It is correct answer but not best practice. By the way, if you have better solution or have better suggestion please advise. I am open to change my answer and publish further improvement to this solution. On very separate note, I like to have clustered index on my Primary Key, which I have not mentioned here as it is out of the scope of this puzzle. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Index, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Statistics

    Read the article

  • SQLAuthority News – A Real Story of Book Getting ‘Out of Stock’ to A 25% Discount Story Available

    - by pinaldave
    As many of my readers may know, I have recently written a few books.  Right now I’d like to talk about SQL Server Interview Questions and Answers (http://bit.ly/sqlinterviewbook ), my newest release. What inspired me to write this book was similar to my motivations for my previous titles – I wanted to help people understand SQL Server concepts and ace interview questions so that they could get a great job they love, as much as I love my own job. If you are new to SQL Server, don’t think I left you out of my book writing efforts. If you are new to the subject or have not had to deal with SQL Server in a long time, this book is perfect for someone who wants or needs a last minute refresher. If you are facing an upcoming interview and want to impress your future bosses, this book is perfect for getting you up to speed in a short time. However, if you are already an expert, you will still find a lot to learn and many pointers and suggestions that go deep into the subject. As I said before, I wrote this book in order to help my community, and I certainly hoped that this book would become popular. However, we decided to print a very limited number of copies to begin with. We did not think that it would sell out since much of the information is available for free online. We could not have been more wrong! We incorrectly estimated what people wanted. We did not realize that there is still a need and an interest for structured learning. So, with great reservations, we printed quite a large number of copies – and it still ran out in 36 hours! We got call from the online store with a request for more copies within 12 hours. But we had printed only as many as we had sent them. There were no extra copies. We finally talked to the printer to get more copies. However, due to festivals and holidays the copies could not be shipped to the online retailer for two days. We knew for sure that they were going to be out of the book for 48 hours. 48 hours – this was very difficult as the book was very highly anticipated. Many people wanted to buy this book quickly, and receive it soon in order to meet a deadline or to study for an upcoming test of their knowledge. But now this book was out of stock on the retail store. The way the online store works is that if the Indian-priced book is not there they list the US version of the book so that buyers will not be disappointed. The problem was that the US price of the book is three times more than the Indian price – which means one has to pay three times as much to buy this book instead of the previous very low price. We received a lot of communication on this subject, here are some examples: We are now businessmen and only focusing on money Why has the price tripled in 36 hours Why we are not honest with the price If the prices will ever come down And some of the letters we cannot post here! Well, finally after 48 hours the Indian stock was finally available online. Thanks to our printer who worked day and night to get all the copies printed. He divided the complete stock in two parts. The first part they sent immediately to online retailer  and the second part they kept with them to sell. Finally, the online retailer got them online promptly as well, and the price returned to normal. Our book once again got in business and became the eighth most popular new release in 36 hours. We appreciate your love and support. Without all of your interest and love we would have never come this far and the book would not be so successful. After thinking about all your support and how patient you were with our online troubles, the online retailer has decided to give an extra 25% discount for a limited time only. I think the 48 hours when the book was out of stock were very horrible and stressful and I’d like to apologize to my loyal readers for the mishap. I hope that the 25% off is enough to sooth any remaining hurt feelings, and that everyone will continue to learn and discover things in the book. Once again thank you so much and I truly hope that you all enjoy reading the book as much as I enjoyed writing it. My book SQL Server Interview Questions and Answers is available now. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, T SQL, Technology

    Read the article

  • SQL SERVER – Solution to Puzzle – Simulate LEAD() and LAG() without Using SQL Server 2012 Analytic Function

    - by pinaldave
    Earlier I wrote a series on SQL Server Analytic Functions of SQL Server 2012. During the series to keep the learning maximum and having fun, we had few puzzles. One of the puzzle was simulating LEAD() and LAG() without using SQL Server 2012 Analytic Function. Please read the puzzle here first before reading the solution : Write T-SQL Self Join Without Using LEAD and LAG. When I was originally wrote the puzzle I had done small blunder and the question was a bit confusing which I corrected later on but wrote a follow up blog post on over here where I describe the give-away. Quick Recap: Generate following results without using SQL Server 2012 analytic functions. I had received so many valid answers. Some answers were similar to other and some were very innovative. Some answers were very adaptive and some did not work when I changed where condition. After selecting all the valid answer, I put them in table and ran RANDOM function on the same and selected winners. Here are the valid answers. No Joins and No Analytic Functions Excellent Solution by Geri Reshef – Winner of SQL Server Interview Questions and Answers (India | USA) WITH T1 AS (SELECT Row_Number() OVER(ORDER BY SalesOrderDetailID) N, s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663)) SELECT SalesOrderID,SalesOrderDetailID,OrderQty, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY N/2) END LeadVal, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY N/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) END LagVal FROM T1 ORDER BY SalesOrderID, SalesOrderDetailID, OrderQty; GO No Analytic Function and Early Bird Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription -- a query to emulate LEAD() and LAG() ;WITH s AS ( SELECT 1 AS ldOffset, -- equiv to 2nd param of LEAD 1 AS lgOffset, -- equiv to 2nd param of LAG NULL AS ldDefVal, -- equiv to 3rd param of LEAD NULL AS lgDefVal, -- equiv to 3rd param of LAG ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLd.SalesOrderDetailID, s.ldDefVal) AS LeadValue, ISNULL( sLg.SalesOrderDetailID, s.lgDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLd ON s.row = sLd.row - s.ldOffset LEFT OUTER JOIN s AS sLg ON s.row = sLg.row + s.lgOffset ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and Partition By Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription /* a query to emulate LEAD() and LAG() */ ;WITH s AS ( SELECT 1 AS LeadOffset, /* equiv to 2nd param of LEAD */ 1 AS LagOffset, /* equiv to 2nd param of LAG */ NULL AS LeadDefVal, /* equiv to 3rd param of LEAD */ NULL AS LagDefVal, /* equiv to 3rd param of LAG */ /* Try changing the values of the 4 integer values above to see their effect on the results */ /* The values given above of 0, 0, null and null behave the same as the default 2nd and 3rd parameters to LEAD() and LAG() */ ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLead.SalesOrderDetailID, s.LeadDefVal) AS LeadValue, ISNULL( sLag.SalesOrderDetailID, s.LagDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLead ON s.row = sLead.row - s.LeadOffset /* Try commenting out this next line when LeadOffset != 0 */ AND s.SalesOrderID = sLead.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LEAD() function */ LEFT OUTER JOIN s AS sLag ON s.row = sLag.row + s.LagOffset /* Try commenting out this next line when LagOffset != 0 */ AND s.SalesOrderID = sLag.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LAG() function */ ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and CTE Usage Excellent Solution by Pravin Patel - Winner of SQL Server Interview Questions and Answers (India | USA) --CTE based solution ; WITH cteMain AS ( SELECT SalesOrderID, SalesOrderDetailID, OrderQty, ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS sn FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, sLead.SalesOrderDetailID AS leadvalue, sLeg.SalesOrderDetailID AS leagvalue FROM cteMain AS m LEFT OUTER JOIN cteMain AS sLead ON sLead.sn = m.sn+1 LEFT OUTER JOIN cteMain AS sLeg ON sLeg.sn = m.sn-1 ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty No Analytic Function and Co-Related Subquery Usage Excellent Solution by Pravin Patel – Winner of SQL Server Interview Questions and Answers (India | USA) -- Co-Related subquery SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, ( SELECT MIN(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID >= m.SalesOrderID AND l.SalesOrderDetailID > m.SalesOrderDetailID ) AS lead, ( SELECT MAX(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID <= m.SalesOrderID AND l.SalesOrderDetailID < m.SalesOrderDetailID ) AS leag FROM Sales.SalesOrderDetail AS m WHERE m.SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty This was one of the most interesting Puzzle on this blog. Giveaway Winners will get following giveaways. Geri Reshef and Pravin Patel SQL Server Interview Questions and Answers (India | USA) DHall Pluralsight 30 days Subscription Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Function, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – PAGELATCH_DT, PAGELATCH_EX, PAGELATCH_KP, PAGELATCH_SH, PAGELATCH_UP – Wait Type – Day 12 of 28

    - by pinaldave
    This is another common wait type. However, I still frequently see people getting confused with PAGEIOLATCH_X and PAGELATCH_X wait types. Actually, there is a big difference between the two. PAGEIOLATCH is related to IO issues, while PAGELATCH is not related to IO issues but is oftentimes linked to a buffer issue. Before we delve deeper in this interesting topic, first let us understand what Latch is. Latches are internal SQL Server locks which can be described as very lightweight and short-term synchronization objects. Latches are not primarily to protect pages being read from disk into memory. It’s a synchronization object for any in-memory access to any portion of a log or data file.[Updated based on comment of Paul Randal] The difference between locks and latches is that locks seal all the involved resources throughout the duration of the transactions (and other processes will have no access to the object), whereas latches locks the resources during the time when the data is changed. This way, a latch is able to maintain the integrity of the data between storage engine and data cache. A latch is a short-living lock that is put on resources on buffer cache and in the physical disk when data is moved in either directions. As soon as the data is moved, the latch is released. Now, let us understand the wait stat type  related to latches. From Book On-Line: PAGELATCH_DT Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Destroy mode. PAGELATCH_EX Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Exclusive mode. PAGELATCH_KP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Keep mode. PAGELATCH_SH Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Shared mode. PAGELATCH_UP Occurs when a task is waiting on a latch for a buffer that is not in an I/O request. The latch request is in Update mode. PAGELATCH_X Explanation: When there is a contention of access of the in-memory pages, this wait type shows up. It is quite possible that some of the pages in the memory are of very high demand. For the SQL Server to access them and put a latch on the pages, it will have to wait. This wait type is usually created at the same time. Additionally, it is commonly visible when the TempDB has higher contention as well. If there are indexes that are heavily used, contention can be created as well, leading to this wait type. Reducing PAGELATCH_X wait: The following counters are useful to understand the status of the PAGELATCH: Average Latch Wait Time (ms): The wait time for latch requests that have to wait. Latch Waits/sec: This is the number of latch requests that could not be granted immediately. Total Latch Wait Time (ms): This is the total latch wait time for latch requests in the last second. If there is TempDB contention, I suggest that you read the blog post of Robert Davis right away. He has written an excellent blog post regarding how to find out TempDB contention. The same blog post explains the terms in the allocation of GAM, SGAM and PFS. If there was a TempDB contention, Paul Randal explains the optimal settings for the TempDB in his misconceptions series. Trace Flag 1118 can be useful but use it very carefully. I totally understand that this blog post is not as clear as my other blog posts. I suggest if this wait stats is on one of your higher wait type. Do leave a comment or send me an email and I will get back to you with my solution for your situation. May the looking at all other wait stats and types together become effective as this wait type can help suggest proper bottleneck in your system. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

    Read the article

  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28

    - by pinaldave
    CXPACKET has to be most popular one of all wait stats. I have commonly seen this wait stat as one of the top 5 wait stats in most of the systems with more than one CPU. Books On-Line: Occurs when trying to synchronize the query processor exchange iterator. You may consider lowering the degree of parallelism if contention on this wait type becomes a problem. CXPACKET Explanation: When a parallel operation is created for SQL Query, there are multiple threads for a single query. Each query deals with a different set of the data (or rows). Due to some reasons, one or more of the threads lag behind, creating the CXPACKET Wait Stat. There is an organizer/coordinator thread (thread 0), which takes waits for all the threads to complete and gathers result together to present on the client’s side. The organizer thread has to wait for the all the threads to finish before it can move ahead. The Wait by this organizer thread for slow threads to complete is called CXPACKET wait. Note that not all the CXPACKET wait types are bad. You might experience a case when it totally makes sense. There might also be cases when this is unavoidable. If you remove this particular wait type for any query, then that query may run slower because the parallel operations are disabled for the query. Reducing CXPACKET wait: We cannot discuss about reducing the CXPACKET wait without talking about the server workload type. OLTP: On Pure OLTP system, where the transactions are smaller and queries are not long but very quick usually, set the “Maximum Degree of Parallelism” to 1 (one). This way it makes sure that the query never goes for parallelism and does not incur more engine overhead. EXEC sys.sp_configure N'cost threshold for parallelism', N'1' GO RECONFIGURE WITH OVERRIDE GO Data-warehousing / Reporting server: As queries will be running for long time, it is advised to set the “Maximum Degree of Parallelism” to 0 (zero). This way most of the queries will utilize the parallel processor, and long running queries get a boost in their performance due to multiple processors. EXEC sys.sp_configure N'cost threshold for parallelism', N'0' GO RECONFIGURE WITH OVERRIDE GO Mixed System (OLTP & OLAP): Here is the challenge. The right balance has to be found. I have taken a very simple approach. I set the “Maximum Degree of Parallelism” to 2, which means the query still uses parallelism but only on 2 CPUs. However, I keep the “Cost Threshold for Parallelism” very high. This way, not all the queries will qualify for parallelism but only the query with higher cost will go for parallelism. I have found this to work best for a system that has OLTP queries and also where the reporting server is set up. Here, I am setting ‘Cost Threshold for Parallelism’ to 25 values (which is just for illustration); you can choose any value, and you can find it out by experimenting with the system only. In the following script, I am setting the ‘Max Degree of Parallelism’ to 2, which indicates that the query that will have a higher cost (here, more than 25) will qualify for parallel query to run on 2 CPUs. This implies that regardless of the number of CPUs, the query will select any two CPUs to execute itself. EXEC sys.sp_configure N'cost threshold for parallelism', N'25' GO EXEC sys.sp_configure N'max degree of parallelism', N'2' GO RECONFIGURE WITH OVERRIDE GO Read all the post in the Wait Types and Queue series. Additionally a must read comment of Jonathan Kehayias. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest you all to read the online book for further clarification. All the discussion of Wait Stats over here is generic and it varies from system to system. It is recommended that you test this on the development server before implementing on the production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

    Read the article

  • SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Introduction – Day 1 of 31

    - by pinaldave
    List of all the Interview Questions and Answers Series blogs Posts covering interview questions and answers always make for interesting reading.  Some people like the subject for their helpful hints and thought provoking subject, and others dislike these posts because they feel it is nothing more than cheating.  I’d like to discuss the pros and cons of a Question and Answer format here. Interview Questions and Answers are Helpful Just like blog posts, books, and articles, interview Question and Answer discussions are learning material.  The popular Dummy’s books or Idiots Guides are not only for “dummies,” but can help everyone relearn the fundamentals.  Question and Answer discussions can serve the same purpose.  You could call this SQL Server Fundamentals or SQL Server 101. I have administrated hundreds of interviews during my career and I have noticed that sometimes an interviewee with several years of experience lacks an understanding of the fundamentals.  These individuals have been in the industry for so long, usually working on a very specific project, that the ABCs of the business have slipped their mind. Or, when a college graduate is looking to get into the industry, he is not expected to have experience since he is just graduated. However, the new grad is expected to have an understanding of fundamentals and theory.  Sometimes after the stress of final exams and graduation, it can be difficult to remember the correct answers to interview questions, though. An interview Question and Answer discussion can be very helpful to both these individuals.  It is simply a way to go back over the building blocks of a topic.  Many times a simple review like this will help “jog” your memory, and all those previously-memorized facts will come flooding back to you.  It is not a way to re-learn a topic, but a way to remind yourself of what you already know. A Question and Answer discussion can also be a way to go over old topics in a more interesting manner.  Especially if you have been working in the industry, or taking lots of classes on the topic, everything you read can sound like a repeat of what you already know.  Going over a topic in a new format can make the material seem fresh and interesting.  And an interested mind will be more engaged and remember more in the end. Interview Questions and Answers are Harmful A common argument against a Question and Answer discussion is that it will give someone a “cheat sheet.” A new guy with relatively little experience can read the interview questions and answers, and then memorize them. When an interviewer asks him the same questions, he will repeat the answers and get the job. Honestly, is he good hire because he memorized the interview questions? Wouldn’t it be better for the interviewer to hire someone with actual experience?  The answer is not as easy as it seems – there are many different factors to be considered. If the interviewer is asking fundamentals-related questions only, he gets the answers he wants to hear, and then hires this first candidate – there is a good chance that he is hiring based on personality rather than experience.  If the interviewer is smart he will ask deeper questions, have more than one person on the interview team, and interview a variety of candidates.  If one interviewee happens to memorize some answers, it usually doesn’t mean he will automatically get the job at the expense of more qualified candidates. Another argument against interview Question and Answers is that it will give candidates a false sense of confidence, and that they will appear more qualified than they are. Well, if that is true, it will not last after the first interview when the candidate is asked difficult questions and he cannot find the answers in the list of interview Questions and Answers.  Besides, confidence is one of the best things to walk into an interview with! In today’s competitive job market, there are often hundreds of candidates applying for the same position.  With so many applicants to choose from, interviewers must make decisions about who to call back and who to hire based on their gut feeling.  One drawback to reading an interview Question and Answer article is that you might sound very boring in your interview – saying the same thing as every single candidate, and parroting answers that sound like someone else wrote them for you – because they did.  However, it is definitely better to go to an interview prepared, just make sure that you give a lot of thought to your answers to make them sound like your own voice.  Remember that you will be hired based on your skills as well as your personality, so don’t think that having all the right answers will make get you hired.  A good interviewee will be prepared, confident, and know how to stand out. My Opinion A list of interview Questions and Answers is really helpful as a refresher or for beginners. To really ace an interview, one needs to have real-world, hands-on experience with SQL Server as well. Interview questions just serve as a starter or easy read for experienced professionals. When I have to learn new technology, I often search online for interview questions and get an idea about the breadth and depth of the technology. Next Action I am going to write about interview Questions and Answers for next 30 days. I have previously written a series of interview questions and answers; now I have re-written them keeping the latest version of SQL Server and current industry progress in mind. If you have faced interesting interview questions or situations, please write to me and I will publish them as a guest post. If you want me to add few more details, leave a comment and I will make sure that I do my best to accommodate. Tomorrow we will start the interview Questions and Answers series, with a few interesting stories, best practices and guest posts. We will have a prize give-away and other awards when the series ends. List of all the Interview Questions and Answers Series blogs Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

    - by pinaldave
    Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • SQL SERVER – 5 Tips for Improving Your Data with expressor Studio

    - by pinaldave
    It’s no secret that bad data leads to bad decisions and poor results.  However, how do you prevent dirty data from taking up residency in your data store?  Some might argue that it’s the responsibility of the person sending you the data.  While that may be true, in practice that will rarely hold up.  It doesn’t matter how many times you ask, you will get the data however they decide to provide it. So now you have bad data.  What constitutes bad data?  There are quite a few valid answers, for example: Invalid date values Inappropriate characters Wrong data Values that exceed a pre-set threshold While it is certainly possible to write your own scripts and custom SQL to identify and deal with these data anomalies, that effort often takes too long and becomes difficult to maintain.  Instead, leveraging an ETL tool like expressor Studio makes the data cleansing process much easier and faster.  Below are some tips for leveraging expressor to get your data into tip-top shape. Tip 1:     Build reusable data objects with embedded cleansing rules One of the new features in expressor Studio 3.2 is the ability to define constraints at the metadata level.  Using expressor’s concept of Semantic Types, you can define reusable data objects that have embedded logic such as constraints for dealing with dirty data.  Once defined, they can be saved as a shared atomic type and then re-applied to other data attributes in other schemas. As you can see in the figure above, I’ve defined a constraint on zip code.  I can then save the constraint rules I defined for zip code as a shared atomic type called zip_type for example.   The next time I get a different data source with a schema that also contains a zip code field, I can simply apply the shared atomic type (shown below) and the previously defined constraints will be automatically applied. Tip 2:     Unlock the power of regular expressions in Semantic Types Another powerful feature introduced in expressor Studio 3.2 is the option to use regular expressions as a constraint.   A regular expression is used to identify patterns within data.   The patterns could be something as simple as a date format or something much more complex such as a street address.  For example, I could define that a valid IP address should be made up of 4 numbers, each 0 to 255, and separated by a period.  So 192.168.23.123 might be a valid IP address whereas 888.777.0.123 would not be.   How can I account for this using regular expressions? A very simple regular expression that would look for any 4 sets of 3 digits separated by a period would be:  ^[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}$ Alternatively, the following would be the exact check for truly valid IP addresses as we had defined above:  ^(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])\.(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9]?[0-9])$ .  In expressor, we would enter this regular expression as a constraint like this: Here we select the corrective action to be ‘Escalate’, meaning that the expressor Dataflow operator will decide what to do.  Some of the options include rejecting the offending record, skipping it, or aborting the dataflow. Tip 3:     Email pattern expressions that might come in handy In the example schema that I am using, there’s a field for email.  Email addresses are often entered incorrectly because people are trying to avoid spam.  While there are a lot of different ways to define what constitutes a valid email address, a quick search online yields a couple of really useful regular expressions for validating email addresses: This one is short and sweet:  \b[A-Z0-9._%+-][email protected][A-Z0-9.-]+\.[A-Z]{2,4}\b (Source: http://www.regular-expressions.info/) This one is more specific about which characters are allowed:  ^([a-zA-Z0-9_\-\.]+)@((\[[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.)|(([a-zA-Z0-9\-]+\.)+))([a-zA-Z]{2,4}|[0-9]{1,3})(\]?)$ (Source: http://regexlib.com/REDetails.aspx?regexp_id=26 ) Tip 4:     Reject “dirty data” for analysis or further processing Yet another feature introduced in expressor Studio 3.2 is the ability to reject records based on constraint violations.  To capture reject records on input, simply specify Reject Record in the Error Handling setting for the Read File operator.  Then attach a Write File operator to the reject port of the Read File operator as such: Next, in the Write File operator, you can configure the expressor operator in a similar way to the Read File.  The key difference would be that the schema needs to be derived from the upstream operator as shown below: Once configured, expressor will output rejected records to the file you specified.  In addition to the rejected records, expressor also captures some diagnostic information that will be helpful towards identifying why the record was rejected.  This makes diagnosing errors much easier! Tip 5:    Use a Filter or Transform after the initial cleansing to finish the job Sometimes you may want to predicate the data cleansing on a more complex set of conditions.  For example, I may only be interested in processing data containing males over the age of 25 in certain zip codes.  Using an expressor Filter operator, you can define the conditional logic which isolates the records of importance away from the others. Alternatively, the expressor Transform operator can be used to alter the input value via a user defined algorithm or transformation.  It also supports the use of conditional logic and data can be rejected based on constraint violations. However, the best tip I can leave you with is to not constrain your solution design approach – expressor operators can be combined in many different ways to achieve the desired results.  For example, in the expressor Dataflow below, I can post-process the reject data from the Filter which did not meet my pre-defined criteria and, if successful, Funnel it back into the flow so that it gets written to the target table. I continue to be impressed that expressor offers all this functionality as part of their FREE expressor Studio desktop ETL tool, which you can download from here.  Their Studio ETL tool is absolutely free and they are very open about saying that if you want to deploy their software on a dedicated Windows Server, you need to purchase their server software, whose pricing is posted on their website. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – A Quick Look at Logging and Ideas around Logging

    - by pinaldave
    This blog post is written in response to the T-SQL Tuesday post on Logging. When someone talks about logging, personally I get lots of ideas about it. I have seen logging as a very generic term. Let me ask you this question first before I continue writing about logging. What is the first thing comes to your mind when you hear word “Logging”? Now ask the same question to the guy standing next to you. I am pretty confident that you will get  a different answer from different people. I decided to do this activity and asked 5 SQL Server person the same question. Question: What is the first thing comes to your mind when you hear the word “Logging”? Strange enough I got a different answer every single time. Let me just list what answer I got from my friends. Let us go over them one by one. Output Clause The very first person replied output clause. Pretty interesting answer to start with. I see what exactly he was thinking. SQL Server 2005 has introduced a new OUTPUT clause. OUTPUT clause has access to inserted and deleted tables (virtual tables) just like triggers. OUTPUT clause can be used to return values to client clause. OUTPUT clause can be used with INSERT, UPDATE, or DELETE to identify the actual rows affected by these statements. Here are some references for Output Clause: OUTPUT Clause Example and Explanation with INSERT, UPDATE, DELETE Reasons for Using Output Clause – Quiz Tips from the SQL Joes 2 Pros Development Series – Output Clause in Simple Examples Error Logs I was expecting someone to mention Error logs when it is about logging. The error log is the most looked place when there is any error either with the application or there is an error with the operating system. I have kept the policy to check my server’s error log every day. The reason is simple – enough time in my career I have figured out that when I am looking at error logs I find something which I was not expecting. There are cases, when I noticed errors in the error log and I fixed them before end user notices it. Other common practices I always tell my DBA friends to do is that when any error happens they should find relevant entries in the error logs and document the same. It is quite possible that they will see the same error in the error log  and able to fix the error based on the knowledge base which they have created. There can be many different kinds of error log files exists in SQL Server as well – 1) SQL Server Error Logs 2) Windows Event Log 3) SQL Server Agent Log 4) SQL Server Profile Log 5) SQL Server Setup Log etc. Here are some references for Error Logs: Recycle Error Log – Create New Log file without Server Restart SQL Error Messages Change Data Capture I got surprised with this answer. I think more than the answer I was surprised by the person who had answered me this one. I always thought he was expert in HTML, JavaScript but I guess, one should never assume about others. Indeed one of the cool logging feature is Change Data Capture. Change Data Capture records INSERTs, UPDATEs, and DELETEs applied to SQL Server tables, and makes a record available of what changed, where, and when, in simple relational ‘change tables’ rather than in an esoteric chopped salad of XML. These change tables contain columns that reflect the column structure of the source table you have chosen to track, along with the metadata needed to understand the changes that have been made. Here are some references for Change Data Capture: Introduction to Change Data Capture (CDC) in SQL Server 2008 Tuning the Performance of Change Data Capture in SQL Server 2008 Download Script of Change Data Capture (CDC) CDC and TRUNCATE – Cannot truncate table because it is published for replication or enabled for Change Data Capture Dynamic Management View (DMV) I like this answer. If asked I would have not come up with DMV right away but in the spirit of the original question, I think DMV does log the data. DMV logs or stores or records the various data and activity on the SQL Server. Dynamic management views return server state information that can be used to monitor the health of a server instance, diagnose problems, and tune performance. One can get plethero of information from DMVs – High Availability Status, Query Executions Details, SQL Server Resources Status etc. Here are some references for Dynamic Management View (DMV): SQL SERVER – Denali – DMV Enhancement – sys.dm_exec_query_stats – New Columns DMV – sys.dm_os_windows_info – Information about Operating System DMV – sys.dm_os_wait_stats Explanation – Wait Type – Day 3 of 28 DMV sys.dm_exec_describe_first_result_set_for_object – Describes the First Result Metadata for the Module Transaction Log Impact Detection Using DMV – dm_tran_database_transactions Log Files I almost flipped with this final answer from my friend. This should be probably the first answer. Yes, indeed log file logs the SQL Server activities. One can write infinite things about log file. SQL Server uses log file with the extension .ldf to manage transactions and maintain database integrity. Log file ensures that valid data is written out to database and system is in a consistent state. Log files are extremely useful in case of the database failures as with the help of full backup file database can be brought in the desired state (point in time recovery is also possible). SQL Server database has three recovery models – 1) Simple, 2) Full and 3) Bulk Logged. Each of the model uses the .ldf file for performing various activities. It is very important to take the backup of the log files (along with full backup) as one never knows when backup of the log file come into the action and save the day! How to Stop Growing Log File Too Big Reduce the Virtual Log Files (VLFs) from LDF file Log File Growing for Model Database – model Database Log File Grew Too Big master Database Log File Grew Too Big SHRINKFILE and TRUNCATE Log File in SQL Server 2008 Can I just say I loved this month’s T-SQL Tuesday Question. It really provoked very interesting conversation around me. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Using expressor Composite Types to Enforce Business Rules

    - by pinaldave
    One of the features that distinguish the expressor Data Integration Platform from other products in the data integration space is its concept of composite types, which provide an effective and easily reusable way to clearly define the structure and characteristics of data within your application.  An important feature of the composite type approach is that it allows you to easily adjust the content of a record to its ultimate purpose.  For example, a record used to update a row in a database table is easily defined to include only the minimum set of columns, that is, a value for the key column and values for only those columns that need to be updated. Much like a class in higher level programming languages, you can also use the composite type as a way to enforce business rules onto your data by encapsulating a datum’s name, data type, and constraints (for example, maximum, minimum, or acceptable values) as a single entity, which ensures that your data can not assume an invalid value.  To what extent you use this functionality is a decision you make when designing your application; the expressor design paradigm does not force this approach on you. Let’s take a look at how these features are used.  Suppose you want to create a group of applications that maintain the employee table in your human resources database. Your table might have a structure similar to the HumanResources.Employee table in the AdventureWorks database.  This table includes two columns, EmployeID and rowguid, that are maintained by the relational database management system; you cannot provide values for these columns when inserting new rows into the table. Additionally, there are columns such as VacationHours and SickLeaveHours that you might choose to update for all employees on a monthly basis, which justifies creation of a dedicated application. By creating distinct composite types for the read, insert and update operations against this table, you can more easily manage this table’s content. When developing this application within expressor Studio, your first task is to create a schema artifact for the database table.  This process is completely driven by a wizard, only requiring that you select the desired database schema and table.  The resulting schema artifact defines the mapping of result set records to a record within the expressor data integration application.  The structure of the record within the expressor application is a composite type that is given the default name CompositeType1.  As you can see in the following figure, all columns from the table are included in the result set and mapped to an identically named attribute in the default composite type. If you are developing an application that needs to read this table, perhaps to prepare a year-end report of employees by department, you would probably not be interested in the data in the rowguid and ModifiedDate columns.  A typical approach would be to drop this unwanted data in a downstream operator.  But using an alternative composite type provides a better approach in which the unwanted data never enters your application. While working in expressor  Studio’s schema editor, simply create a second composite type within the same schema artifact, which you could name ReadTable, and remove the attributes corresponding to the unwanted columns. The value of an alternative composite type is even more apparent when you want to insert into or update the table.  In the composite type used to insert rows, remove the attributes corresponding to the EmployeeID primary key and rowguid uniqueidentifier columns since these values are provided by the relational database management system. And to update just the VacationHours and SickLeaveHours columns, use a composite type that includes only the attributes corresponding to the EmployeeID, VacationHours, SickLeaveHours and ModifiedDate columns. By specifying this schema artifact and composite type in a Write Table operator, your upstream application need only deal with the four required attributes and there is no risk of unintentionally overwriting a value in a column that does not need to be updated. Now, what about the option to use the composite type to enforce business rules?  If you review the composition of the default composite type CompositeType1, you will note that the constraints defined for many of the attributes mirror the table column specifications.  For example, the maximum number of characters in the NationaIDNumber, LoginID and Title attributes is equivalent to the maximum width of the target column, and the size of the MaritalStatus and Gender attributes is limited to a single character as required by the table column definition.  If your application code leads to a violation of these constraints, an error will be raised.  The expressor design paradigm then allows you to handle the error in a way suitable for your application.  For example, a string value could be truncated or a numeric value could be rounded. Moreover, you have the option of specifying additional constraints that support business rules unrelated to the table definition. Let’s assume that the only acceptable values for marital status are S, M, and D.  Within the schema editor, double-click on the MaritalStatus attribute to open the Edit Attribute window.  Then click the Allowed Values checkbox and enter the acceptable values into the Constraint Value text box. The schema editor is updated accordingly. There is one more option that the expressor semantic type paradigm supports.  Since the MaritalStatus attribute now clearly specifies how this type of information should be represented (a single character limited to S, M or D), you can convert this attribute definition into a shared type, which will allow you to quickly incorporate this definition into another composite type or into the description of an output record from a transform operator. Again, double-click on the MaritalStatus attribute and in the Edit Attribute window, click Convert, which opens the Share Local Semantic Type window that you use to name this shared type.  There’s no requirement that you give the shared type the same name as the attribute from which it was derived.  You should supply a name that makes it obvious what the shared type represents. In this posting, I’ve overviewed the expressor semantic type paradigm and shown how it can be used to make your application development process more productive.  The beauty of this feature is that you choose when and to what extent you utilize the functionality, but I’m certain that if you opt to follow this approach your efforts will become more efficient and your work will progress more quickly.  As always, I encourage you to download and evaluate expressor Studio for your current and future data integration needs. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • SQLAuthority Book Review – DBA Survivor: Become a Rock Star DBA

    - by pinaldave
    DBA Survivor: Become a Rock Star DBA – Thomas LaRock Link to Amazon Link to Flipkart First of all, I thank all my readers when I wrote that I could not get this book in any local book stores, because they offered me to send a copy of this good book. A very special mention goes to Sripada and Jayesh for they gave so much effort in finding my home address and sending me the hard copy. Before, I did not have the copy of the book, but now I have two of it already! It surprises me how my readers were able to find my home address, which I have not publicly shared. Quick Review: This is indeed a one easy-to-read and fun book. We all work day and night with technology yet we should not forget to show our love and care for our family at home. For our souls that starve for peace and guidance, this one book is the “it” book for all the technology enthusiasts. Though this book was specifically written for DBAs, the reach is not limited to DBAs only because the lessons incorporated in it actually applies to all. This is one of the most motivating technical books I have read. Detailed Review: Let us go over a few questions first: Who wants to be as famous as rockstars in the field of Database Administration? How can one learn what it takes to become a top notch software developer? If you are a beginner in your field, how will you go to next level? Your boss may be very kind or like Dilbert’s Boss, what will you do? How do you keep growing when Eco-system around you does not support you? You are almost at top but there is someone else at the TOP, what do you do and how do you avoid office politics? As a database developer what should be your basic responsibility? and many more… I was able to completely read book in one sitting and I loved it. Before I continue with my opinion, I want to echo the opinion of Kevin Kline who has written the Forward of the book. He has truly suggested that “You hold in your hands a collection of insights and wisdom on the topic of database administration gained through many years of hard-won experience, long nights of study, and direct mentorship under some of the industry’s most talented database professionals and information technology (IT) experts.” Today, IT field is getting bigger and better, while talking about terabytes of the database becomes “more” normal every single day. The gods and demigods of database professionals are taking care of these large scale databases and are carefully maintaining them. In this world, there are only a few beginnings on the first step. There are many experts in different technology fields who are asked to address the issues with databases. There is YOU and ME, who is just new to this work. So we ask ourselves WHERE to begin and HOW to begin. We adore and follow the religion of our rockstars, but oftentimes we really have no idea about their background and their struggles. Every rockstar has his success story which needs to be digested before learning his tricks and tips. This book starts with the same note and teaches the two most important lessons for anybody who wants to be a DBA Rockstar –  to focus on their single goal of learning and to excel the technology. The story starts with three simple guidelines – Get Prepared, Get Trained, Get Certified. Once a person learns the skills, and then, it would be about time that he needs to enrich or to improve those skills you have learned. I am sure that the right opportunity will come finding themselves and they will not have to go run behind it. However, the real challenge for any person is the first day or first week. A new employee, no matter how much experienced he is, sometimes has no clue about what should one do at new job. Chapter 2 and chapter 3 precisely talk about what one should do as soon as the new job begins. It is also written with keeping the fact in focus that each job can be very much different but there are few infrastructure setups and programming concepts are the same. Learning basics of database was really interesting. I like to focus on the roots of any technology. It is important to understand the structure of the database before suggesting what indexes needs to be created, the same way this book covers the most essential knowledge one must learn by most database developers. I think the title of the fourth chapter is my favorite sentence in this book. I can see that I will be saying this again and again in the future – “A Development Server Is a Production Server to a Developer“. I have worked in the software industry for almost 8 years now and I have seen so many developers sitting on their chairs and waiting for instructions from their lead about how to improve the code or what to do the next. When I talk to them, I suggest that the experiment with their server and try various techniques. I think they all should understand that for them, a development server is their production server and needs to pay proper attention to the code from the beginning. There should be NO any inappropriate code from the beginning. One has to fully focus and give their best, if they are not sure they should ask but should do something and stay active. Chapter 5 and 6 talks about two essential skills for any developer and database administration – what are the ethics of developers when they are working with production server and how to support software which is running on the production server. I have met many people who know the theory by heart but when put in front of keyboard they do not know where to start. The first thing they do opening the browser and searching online, instead of opening SQL Server Management Studio. This can very well happen to anybody who is experienced as well. Chapter 5 and 6 addresses that situation as well includes the handy scripts which can solve almost all the basic trouble shooting issues. “Where’s the Buffet?” By far, this is the best chapter in this book. If you have ever met me, you would know that I love food. I think after reading this chapter, I felt Thomas has written this just keeping me in mind. I think there will be many other people who feel the same way, too. Even my wife who read this chapter thought this was specifically written for me. I will not talk any more about this chapter as this is one must read chapter. And of course this is about real ‘FOOD‘. I am an SQL Server Trainer and Consultant and I totally agree with the point made in the chapter 8 of this book. Yes, it says here that what is necessary to train employees and people. Millions of dollars worth the labor is continuously done in the world which has faults and incorrect. Once something goes wrong, very expensive consultant comes in and fixes the problem. This whole cycle which can be stopped and improved if proper training is done. There is plenty of free trainings available as well, if one cannot afford paid training. “Connect. Learn. Share” – I think this is a great summary and bird’s eye view of this book. Networking is the key. Everything which is discussed in this book can be taken to next level if one properly uses this tips and continuously grow with it. Connecting with others, helping learn each other and building the good knowledge sharing environment should be the goal of everyone. Before I end the review I want to share a real experience. I have personally met one DBA who has worked in a single department in a company for so long that when he was put in a different department in his company due to closing that department, he could not adjust and quit the job despite the same people and company around him. Adjusting in the new environment gets much tougher as one person gets more and more experienced. This book precisely addresses the same issue along with their solutions. I just cannot stop comparing the book with my personal journey. I found so many things which are coincidently in the book is written as how we developer and DBA think. I must express special thanks to Thomas for taking time in his personal life and write this book for us. This book is indeed a book for everybody who wants to grow healthy in the tough and competitive environment. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, SQLServer, T SQL, Technology

    Read the article

  • SQL SERVER – Import CSV into Database – Transferring File Content into a Database Table using CSVexpress

    - by pinaldave
    One of the most common data integration tasks I run into is a desire to move data from a file into a database table.  Generally the user is familiar with his data, the structure of the file, and the database table, but is unfamiliar with data integration tools and therefore views this task as something that is difficult.  What these users really need is a point and click approach that minimizes the learning curve for the data integration tool.  This is what CSVexpress (www.CSVexpress.com) is all about!  It is based on expressor Studio, a data integration tool I’ve been reviewing over the last several months. With CSVexpress, moving data between data sources can be as simple as providing the database connection details, describing the structure of the incoming and outgoing data and then connecting two pre-programmed operators.   There’s no need to learn the intricacies of the data integration tool or to write code.  Let’s look at an example. Suppose I have a comma separated value data file with data similar to the following, which is a listing of terminated employees that includes their hiring and termination date, department, job description, and final salary. EMP_ID,STRT_DATE,END_DATE,JOB_ID,DEPT_ID,SALARY 102,13-JAN-93,24-JUL-98 17:00,Programmer,60,"$85,000" 101,21-SEP-89,27-OCT-93 17:00,Account Representative,110,"$65,000" 103,28-OCT-93,15-MAR-97 17:00,Account Manager,110,"$75,000" 304,17-FEB-96,19-DEC-99 17:00,Marketing,20,"$45,000" 333,24-MAR-98,31-DEC-99 17:00,Data Entry Clerk,50,"$35,000" 100,17-SEP-87,17-JUN-93 17:00,Administrative Assistant,90,"$40,000" 334,24-MAR-98,31-DEC-98 17:00,Sales Representative,80,"$40,000" 400,01-JAN-99,31-DEC-99 17:00,Sales Manager,80,"$55,000" Notice the concise format used for the date values, the fact that the termination date includes both date and time information, and that the salary is clearly identified as money by the dollar sign and digit grouping.  In moving this data to a database table I want to express the dates using a format that includes the century since it’s obvious that this listing could include employees who left the company in both the 20th and 21st centuries, and I want the salary to be stored as a decimal value without the currency symbol and grouping character.  Most data integration tools would require coding within a transformation operation to effect these changes, but not expressor Studio.  Directives for these modifications are included in the description of the incoming data. Besides starting the expressor Studio tool and opening a project, the first step is to create connection artifacts, which describe to expressor where data is stored.  For this example, two connection artifacts are required: a file connection, which encapsulates the file system location of my file; and a database connection, which encapsulates the database connection information.  With expressor Studio, I use wizards to create these artifacts. First click New Connection > File Connection in the Home tab of expressor Studio’s ribbon bar, which starts the File Connection wizard.  In the first window, I enter the path to the directory that contains the input file.  Note that the file connection artifact only specifies the file system location, not the name of the file. Then I click Next and enter a meaningful name for this connection artifact; clicking Finish closes the wizard and saves the artifact. To create the Database Connection artifact, I must know the location of, or instance name, of the target database and have the credentials of an account with sufficient privileges to write to the target table.  To use expressor Studio’s features to the fullest, this account should also have the authority to create a table. I click the New Connection > Database Connection in the Home tab of expressor Studio’s ribbon bar, which starts the Database Connection wizard.  expressor Studio includes high-performance drivers for many relational database management systems, so I can simply make a selection from the “Supplied database drivers” drop down control.  If my desired RDBMS isn’t listed, I can optionally use an existing ODBC DSN by selecting the “Existing DSN” radio button. In the following window, I enter the connection details.  With Microsoft SQL Server, I may choose to use Windows Authentication rather than rather than account credentials.  After clicking Next, I enter a meaningful name for this connection artifact and clicking Finish closes the wizard and saves the artifact. Now I create a schema artifact, which describes the structure of the file data.  When expressor reads a file, all data fields are typed as strings.  In some use cases this may be exactly what is needed and there is no need to edit the schema artifact.  But in this example, editing the schema artifact will be used to specify how the data should be transformed; that is, reformat the dates to include century designations, change the employee and job ID’s to integers, and convert the salary to a decimal value. Again a wizard is used to create the schema artifact.  I click New Schema > Delimited Schema in the Home tab of expressor Studio’s ribbon bar, which starts the Database Connection wizard.  In the first window, I click Get Data from File, which then displays a listing of the file connections in the project.  When I click on the file connection I previously created, a browse window opens to this file system location; I then select the file and click Open, which imports 10 lines from the file into the wizard. I now view the file’s content and confirm that the appropriate delimiter characters are selected in the “Field Delimiter” and “Record Delimiter” drop down controls; then I click Next. Since the input file includes a header row, I can easily indicate that fields in the file should be identified through the corresponding header value by clicking “Set All Names from Selected Row. “ Alternatively, I could enter a different identifier into the Field Details > Name text box.  I click Next and enter a meaningful name for this schema artifact; clicking Finish closes the wizard and saves the artifact. Now I open the schema artifact in the schema editor.  When I first view the schema’s content, I note that the types of all attributes in the Semantic Type (the right-hand panel) are strings and that the attribute names are the same as the field names in the data file.  To change an attribute’s name and type, I highlight the attribute and click Edit in the Attributes grouping on the Schema > Edit tab of the editor’s ribbon bar.  This opens the Edit Attribute window; I can change the attribute name and select the desired type from the “Data type” drop down control.  In this example, I change the name of each attribute to the name of the corresponding database table column (EmployeeID, StartingDate, TerminationDate, JobDescription, DepartmentID, and FinalSalary).  Then for the EmployeeID and DepartmentID attributes, I select Integer as the data type, for the StartingDate and TerminationDate attributes, I select Datetime as the data type, and for the FinalSalary attribute, I select the Decimal type. But I can do much more in the schema editor.  For the datetime attributes, I can set a constraint that ensures that the data adheres to some predetermined specifications; a starting date must be later than January 1, 1980 (the date on which the company began operations) and a termination date must be earlier than 11:59 PM on December 31, 1999.  I simply select the appropriate constraint and enter the value (1980-01-01 00:00 as the starting date and 1999-12-31 11:59 as the termination date). As a last step in setting up these datetime conversions, I edit the mapping, describing the format of each datetime type in the source file. I highlight the mapping line for the StartingDate attribute and click Edit Mapping in the Mappings grouping on the Schema > Edit tab of the editor’s ribbon bar.  This opens the Edit Mapping window in which I either enter, or select, a format that describes how the datetime values are represented in the file.  Note the use of Y01 as the syntax for the year.  This syntax is the indicator to expressor Studio to derive the century by setting any year later than 01 to the 20th century and any year before 01 to the 21st century.  As each datetime value is read from the file, the year values are transformed into century and year values. For the TerminationDate attribute, my format also indicates that the datetime value includes hours and minutes. And now to the Salary attribute. I open its mapping and in the Edit Mapping window select the Currency tab and the “Use currency” check box.  This indicates that the file data will include the dollar sign (or in Europe the Pound or Euro sign), which should be removed. And on the Grouping tab, I select the “Use grouping” checkbox and enter 3 into the “Group size” text box, a comma into the “Grouping character” text box, and a decimal point into the “Decimal separator” character text box. These entries allow the string to be properly converted into a decimal value. By making these entries into the schema that describes my input file, I’ve specified how I want the data transformed prior to writing to the database table and completely removed the requirement for coding within the data integration application itself. Assembling the data integration application is simple.  Onto the canvas I drag the Read File and Write Table operators, connecting the output of the Read File operator to the input of the Write Table operator. Next, I select the Read File operator and its Properties panel opens on the right-hand side of expressor Studio.  For each property, I can select an appropriate entry from the corresponding drop down control.  Clicking on the button to the right of the “File name” text box opens the file system location specified in the file connection artifact, allowing me to select the appropriate input file.  I indicate also that the first row in the file, the header row, should be skipped, and that any record that fails one of the datetime constraints should be skipped. I then select the Write Table operator and in its Properties panel specify the database connection, normal for the “Mode,” and the “Truncate” and “Create Missing Table” options.  If my target table does not yet exist, expressor will create the table using the information encapsulated in the schema artifact assigned to the operator. The last task needed to complete the application is to create the schema artifact used by the Write Table operator.  This is extremely easy as another wizard is capable of using the schema artifact assigned to the Read Table operator to create a schema artifact for the Write Table operator.  In the Write Table Properties panel, I click the drop down control to the right of the “Schema” property and select “New Table Schema from Upstream Output…” from the drop down menu. The wizard first displays the table description and in its second screen asks me to select the database connection artifact that specifies the RDBMS in which the target table will exist.  The wizard then connects to the RDBMS and retrieves a list of database schemas from which I make a selection.  The fourth screen gives me the opportunity to fine tune the table’s description.  In this example, I set the width of the JobDescription column to a maximum of 40 characters and select money as the type of the LastSalary column.  I also provide the name for the table. This completes development of the application.  The entire application was created through the use of wizards and the required data transformations specified through simple constraints and specifications rather than through coding.  To develop this application, I only needed a basic understanding of expressor Studio, a level of expertise that can be gained by working through a few introductory tutorials.  expressor Studio is as close to a point and click data integration tool as one could want and I urge you to try this product if you have a need to move data between files or from files to database tables. Check out CSVexpress in more detail.  It offers a few basic video tutorials and a preview of expressor Studio 3.5, which will support the reading and writing of data into Salesforce.com. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • SQLAuthority News – Presented Technical Session at DevReach 2013, Sofia, Bulgaria – Oct 1, 2013

    - by Pinal Dave
    Earlier this month, I had a fantastic time presenting at DevReach 2013, in Sofia, Bulgaria on Oct 1, 2013. DevReach strives to be the premier developer conference in Central and Eastern Europe. It is organized annually in Sofia, Bulgaria. The 8th edition of the conference is moving to a new and bigger venue: Sofia Event Center. In my career, I have presented over 9 different countries (India, USA, Canada, Singapore, Hong Kong, Malaysia, Sri Lanka, Nepal, Thailand), this was the first time for me to present in Europe. DevReach was perfect places to start my journey in Europe as an evangelist. The event was one of the most organized event I have ever come across in my life. The DevRech organization team had perfected every minute detail of the event to perfection. After the event was over I had the opportunity to see Sofia for one day. I presented with one of my most favorite Database Worst Practices Session. Pinal presenting at DevReach 2013, Sofia, Bulgaria DevReach 2013 DevReach 2013 DevReach 2013 Pinal presenting at DevReach 2013, Sofia, Bulgaria Pinal presenting at DevReach 2013, Sofia, Bulgaria Pinal Dave and Stephen Forte at Pluralsight Booth at DevReach 2013, Sofia, Bulgaria Pinal on City Tour of Sofia, Bulgaria Pinal on City Tour of Sofia, Bulgaria Pinal on City Tour of Sofia, Bulgaria Pinal on City Tour of Sofia, Bulgaria Pinal on City Tour of Sofia, Bulgaria Session Title: Secrets of SQL Server: Database Worst Practices Abstract: “Oh my God! What did I do?” Chances are you have heard, or even uttered, this expression. This demo-oriented session will show many examples where database professionals were dumbfounded by their own mistakes, and could even bring back memories of your own early DBA days. The goal of this session is to expose the small details that can be dangerous to the production environment and SQL Server as a whole, as well as talk about worst practices and how to avoid them. Shedding light on some of these perils and the tricks to avoid them may even save your current job. Thanks to Team Telerik for making this one of the best event in my life. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: About Me, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, T SQL

    Read the article

  • SQL SERVER – Securing TRUNCATE Permissions in SQL Server

    - by pinaldave
    Download the Script of this article from here. On December 11, 2010, Vinod Kumar, a Databases & BI technology evangelist from Microsoft Corporation, graced Ahmedabad by spending some time with the Community during the Community Tech Days (CTD) event. As he was running through a few demos, Vinod asked the audience one of the most fundamental and common interview questions – “What is the difference between a DELETE and TRUNCATE?“ Ahmedabad SQL Server User Group Expert Nakul Vachhrajani has come up with excellent solutions of the same. I must congratulate Nakul for this excellent solution and as a encouragement to User Group member, I am publishing the same article over here. Nakul Vachhrajani is a Software Specialist and systems development professional with Patni Computer Systems Limited. He has functional experience spanning legacy code deprecation, system design, documentation, development, implementation, testing, maintenance and support of complex systems, providing business intelligence solutions, database administration, performance tuning, optimization, product management, release engineering, process definition and implementation. He has comprehensive grasp on Database Administration, Development and Implementation with MS SQL Server and C, C++, Visual C++/C#. He has about 6 years of total experience in information technology. Nakul is an member of the Ahmedabad and Gandhinagar SQL Server User Groups, and actively contributes to the community by actively participating in multiple forums and websites like SQLAuthority.com, BeyondRelational.com, SQLServerCentral.com and many others. Please note: The opinions expressed herein are Nakul own personal opinions and do not represent his employer’s view in anyway. All data from everywhere here on Earth go through a series of  four distinct operations, identified by the words: CREATE, READ, UPDATE and DELETE, or simply, CRUD. Putting in Microsoft SQL Server terms, is the process goes like this: INSERT, SELECT, UPDATE and DELETE/TRUNCATE. Quite a few interesting responses were received and evaluated live during the session. To summarize them, the most important similarity that came out was that both DELETE and TRUNCATE participate in transactions. The major differences (not all) that came out of the exercise were: DELETE: DELETE supports a WHERE clause DELETE removes rows from a table, row-by-row Because DELETE moves row-by-row, it acquires a row-level lock Depending upon the recovery model of the database, DELETE is a fully-logged operation. Because DELETE moves row-by-row, it can fire off triggers TRUNCATE: TRUNCATE does not support a WHERE clause TRUNCATE works by directly removing the individual data pages of a table TRUNCATE directly occupies a table-level lock. (Because a lock is acquired, and because TRUNCATE can also participate in a transaction, it has to be a logged operation) TRUNCATE is, therefore, a minimally-logged operation; again, this depends upon the recovery model of the database Triggers are not fired when TRUNCATE is used (because individual row deletions are not logged) Finally, Vinod popped the big homework question that must be critically analyzed: “We know that we can restrict a DELETE operation to a particular user, but how can we restrict the TRUNCATE operation to a particular user?” After returning home and having a nice cup of coffee, I noticed that my gray cells immediately started to work. Below was the result of my research. As what is always said, the devil is in the details. Upon looking at the Permissions section for the TRUNCATE statement in Books On Line, the following jumps right out: “The minimum permission required is ALTER on table_name. TRUNCATE TABLE permissions default to the table owner, members of the sysadmin fixed server role, and the db_owner and db_ddladmin fixed database roles, and are not transferable. However, you can incorporate the TRUNCATE TABLE statement within a module, such as a stored procedure, and grant appropriate permissions to the module using the EXECUTE AS clause.“ Now, what does this mean? Unlike DELETE, one cannot directly assign permissions to a user/set of users allowing or revoking TRUNCATE rights. However, there is a way to circumvent this. It is important to recall that in Microsoft SQL Server, database engine security surrounds the concept of a “securable”, which is any object like a table, stored procedure, trigger, etc. Rights are assigned to a principal on a securable. Refer to the image below (taken from the SQL Server Books On Line). urable”, which is any object like a table, stored procedure, trigger, etc. Rights are assigned to a principal on a securable. Refer to the image below (taken from the SQL Server Books On Line). SETTING UP THE ENVIRONMENT – (01A_Truncate Table Permissions.sql) Script Provided at the end of the article. By the end of this demo, one will be able to do all the CRUD operations, except the TRUNCATE, and the other will only be able to execute the TRUNCATE. All you will need for this test is any edition of SQL Server 2008. (With minor changes, these scripts can be made to work with SQL 2005.) We begin by creating the following: 1.       A test database 2.        Two database roles: associated logins and users 3.       Switch over to the test database and create a test table. Then, add some data into it. I am using row constructors, which is new to SQL 2008. Creating the modules that will be used to enforce permissions 1.       We have already created one of the modules that we will be assigning permissions to. That module is the table: TruncatePermissionsTest 2.       We will now create two stored procedures; one is for the DELETE operation and the other for the TRUNCATE operation. Please note that for all practical purposes, the end result is the same – all data from the table TruncatePermissionsTest is removed Assigning the permissions Now comes the most important part of the demonstration – assigning permissions. A permissions matrix can be worked out as under: To apply the security rights, we use the GRANT and DENY clauses, as under: That’s it! We are now ready for our big test! THE TEST (01B_Truncate Table Test Queries.sql) Script Provided at the end of the article. I will now need two separate SSMS connections, one with the login AllowedTruncate and the other with the login RestrictedTruncate. Running the test is simple; all that’s required is to run through the script – 01B_Truncate Table Test Queries.sql. What I will demonstrate here via screen-shots is the behavior of SQL Server when logged in as the AllowedTruncate user. There are a few other combinations than what are highlighted here. I will leave the reader the right to explore the behavior of the RestrictedTruncate user and these additional scenarios, as a form of self-study. 1.       Testing SELECT permissions 2.       Testing TRUNCATE permissions (Remember, “deny by default”?) 3.       Trying to circumvent security by trying to TRUNCATE the table using the stored procedure Hence, we have now proved that a user can indeed be assigned permissions to specifically assign TRUNCATE permissions. I also hope that the above has sparked curiosity towards putting some security around the probably “destructive” operations of DELETE and TRUNCATE. I would like to wish each and every one of the readers a very happy and secure time with Microsoft SQL Server. (Please find the scripts – 01A_Truncate Table Permissions.sql and 01B_Truncate Table Test Queries.sql that have been used in this demonstration. Please note that these scripts contain purely test-level code only. These scripts must not, at any cost, be used in the reader’s production environments). 01A_Truncate Table Permissions.sql /* ***************************************************************************************************************** Developed By          : Nakul Vachhrajani Functionality         : This demo is focused on how to allow only TRUNCATE permissions to a particular user How to Use            : 1. Run through, step-by-step through the sequence till Step 08 to create a test database 2. Switch over to the "Truncate Table Test Queries.sql" and execute it step-by-step in two different SSMS windows, one where you have logged in as 'RestrictedTruncate', and the other as 'AllowedTruncate' 3. Come back to "Truncate Table Permissions.sql" 4. Execute Step 10 to cleanup! Modifications         : December 13, 2010 - NAV - Updated to add a security matrix and improve code readability when applying security December 12, 2010 - NAV - Created ***************************************************************************************************************** */ -- Step 01: Create a new test database CREATE DATABASE TruncateTestDB GO USE TruncateTestDB GO -- Step 02: Add roles and users to demonstrate the security of the Truncate operation -- 2a. Create the new roles CREATE ROLE AllowedTruncateRole; GO CREATE ROLE RestrictedTruncateRole; GO -- 2b. Create new logins CREATE LOGIN AllowedTruncate WITH PASSWORD = '[email protected]', CHECK_POLICY = ON GO CREATE LOGIN RestrictedTruncate WITH PASSWORD = '[email protected]', CHECK_POLICY = ON GO -- 2c. Create new Users using the roles and logins created aboave CREATE USER TruncateUser FOR LOGIN AllowedTruncate WITH DEFAULT_SCHEMA = dbo GO CREATE USER NoTruncateUser FOR LOGIN RestrictedTruncate WITH DEFAULT_SCHEMA = dbo GO -- 2d. Add the newly created login to the newly created role sp_addrolemember 'AllowedTruncateRole','TruncateUser' GO sp_addrolemember 'RestrictedTruncateRole','NoTruncateUser' GO -- Step 03: Change over to the test database USE TruncateTestDB GO -- Step 04: Create a test table within the test databse CREATE TABLE TruncatePermissionsTest (Id INT IDENTITY(1,1), Name NVARCHAR(50)) GO -- Step 05: Populate the required data INSERT INTO TruncatePermissionsTest VALUES (N'Delhi'), (N'Mumbai'), (N'Ahmedabad') GO -- Step 06: Encapsulate the DELETE within another module CREATE PROCEDURE proc_DeleteMyTable WITH EXECUTE AS SELF AS DELETE FROM TruncateTestDB..TruncatePermissionsTest GO -- Step 07: Encapsulate the TRUNCATE within another module CREATE PROCEDURE proc_TruncateMyTable WITH EXECUTE AS SELF AS TRUNCATE TABLE TruncateTestDB..TruncatePermissionsTest GO -- Step 08: Apply Security /* *****************************SECURITY MATRIX*************************************** =================================================================================== Object                   | Permissions |                 Login |             | AllowedTruncate   |   RestrictedTruncate |             |User:NoTruncateUser|   User:TruncateUser =================================================================================== TruncatePermissionsTest  | SELECT,     |      GRANT        |      (Default) | INSERT,     |                   | | UPDATE,     |                   | | DELETE      |                   | -------------------------+-------------+-------------------+----------------------- TruncatePermissionsTest  | ALTER       |      DENY         |      (Default) -------------------------+-------------+----*/----------------+----------------------- proc_DeleteMyTable | EXECUTE | GRANT | DENY -------------------------+-------------+-------------------+----------------------- proc_TruncateMyTable | EXECUTE | DENY | GRANT -------------------------+-------------+-------------------+----------------------- *****************************SECURITY MATRIX*************************************** */ /* Table: TruncatePermissionsTest*/ GRANT SELECT, INSERT, UPDATE, DELETE ON TruncateTestDB..TruncatePermissionsTest TO NoTruncateUser GO DENY ALTER ON TruncateTestDB..TruncatePermissionsTest TO NoTruncateUser GO /* Procedure: proc_DeleteMyTable*/ GRANT EXECUTE ON TruncateTestDB..proc_DeleteMyTable TO NoTruncateUser GO DENY EXECUTE ON TruncateTestDB..proc_DeleteMyTable TO TruncateUser GO /* Procedure: proc_TruncateMyTable*/ DENY EXECUTE ON TruncateTestDB..proc_TruncateMyTable TO NoTruncateUser GO GRANT EXECUTE ON TruncateTestDB..proc_TruncateMyTable TO TruncateUser GO -- Step 09: Test --Switch over to the "Truncate Table Test Queries.sql" and execute it step-by-step in two different SSMS windows: --    1. one where you have logged in as 'RestrictedTruncate', and --    2. the other as 'AllowedTruncate' -- Step 10: Cleanup sp_droprolemember 'AllowedTruncateRole','TruncateUser' GO sp_droprolemember 'RestrictedTruncateRole','NoTruncateUser' GO DROP USER TruncateUser GO DROP USER NoTruncateUser GO DROP LOGIN AllowedTruncate GO DROP LOGIN RestrictedTruncate GO DROP ROLE AllowedTruncateRole GO DROP ROLE RestrictedTruncateRole GO USE MASTER GO DROP DATABASE TruncateTestDB GO 01B_Truncate Table Test Queries.sql /* ***************************************************************************************************************** Developed By          : Nakul Vachhrajani Functionality         : This demo is focused on how to allow only TRUNCATE permissions to a particular user How to Use            : 1. Switch over to this from "Truncate Table Permissions.sql", Step #09 2. Execute this step-by-step in two different SSMS windows a. One where you have logged in as 'RestrictedTruncate', and b. The other as 'AllowedTruncate' 3. Return back to "Truncate Table Permissions.sql" 4. Execute Step 10 to cleanup! Modifications         : December 12, 2010 - NAV - Created ***************************************************************************************************************** */ -- Step 09A: Switch to the test database USE TruncateTestDB GO -- Step 09B: Ensure that we have valid data SELECT * FROM TruncatePermissionsTest GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 1 -- The SELECT permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. --Step 09C: Attempt to Truncate Data from the table without using the stored procedure TRUNCATE TABLE TruncatePermissionsTest GO -- (Expected: Following error will occur) --  Msg 1088, Level 16, State 7, Line 2 --  Cannot find the object "TruncatePermissionsTest" because it does not exist or you do not have permissions. -- Step 09D:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'London'), (N'Paris'), (N'Berlin') GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 1 -- The INSERT permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. --Step 09E: Attempt to Truncate Data from the table using the stored procedure EXEC proc_TruncateMyTable GO -- (Expected: Will execute successfully with 'AllowedTruncate' user, will error out as under with 'RestrictedTruncate') -- Msg 229, Level 14, State 5, Procedure proc_TruncateMyTable, Line 1 -- The EXECUTE permission was denied on the object 'proc_TruncateMyTable', database 'TruncateTestDB', schema 'dbo'. -- Step 09F:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'Madrid'), (N'Rome'), (N'Athens') GO --Step 09G: Attempt to Delete Data from the table without using the stored procedure DELETE FROM TruncatePermissionsTest GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Line 2 -- The DELETE permission was denied on the object 'TruncatePermissionsTest', database 'TruncateTestDB', schema 'dbo'. -- Step 09H:Regenerate Test Data INSERT INTO TruncatePermissionsTest VALUES (N'Spain'), (N'Italy'), (N'Greece') GO --Step 09I: Attempt to Delete Data from the table using the stored procedure EXEC proc_DeleteMyTable GO -- (Expected: Following error will occur if logged in as "AllowedTruncate") -- Msg 229, Level 14, State 5, Procedure proc_DeleteMyTable, Line 1 -- The EXECUTE permission was denied on the object 'proc_DeleteMyTable', database 'TruncateTestDB', schema 'dbo'. --Step 09J: Close this SSMS window and return back to "Truncate Table Permissions.sql" Thank you Nakul to take up the challenge and prove that Ahmedabad and Gandhinagar SQL Server User Group has talent to solve difficult problems. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Pinal Dave, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Security, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • SQL SERVER – Transcript of Learning SQL Server Performance: Indexing Basics – Interview of Vinod Kumar by Pinal Dave

    - by pinaldave
    Recently I just wrote a blog post on about Learning SQL Server Performance: Indexing Basics and I received lots of request that if we can share some insight into the course. Here is 200 seconds interview of Vinod Kumar I took right after completing the course. We have few free codes to watch the course, please your comment at http://facebook.com/SQLAuth and we will few of first ones, we will send the code. There are many people who said they would like to read the transcript of the video. Here I have generated the same. Pinal: Vinod, we recently released this course, SQL Server Indexing. It is about performance tuning. So tell me – how do indexes help performance? Vinod: I think what happens in the industry when it comes to performance is that developers and DBAs look at indexes first.  So that’s the first step for any performance tuning exercise, indexing is one of the most critical aspects and it is important to learn it the right way. Pinal: Correct. So what you mean to say is that if you know indexing you can pretty much tune any server and query. Vinod: So I might contradict my false statement now. Indexing is usually a stepping stone but it does not lead you to the end. But it’s good to start with indexing and there are lots of nuances to indexing that you need to understand, like how SQL uses indexing and how performance can improve because of the strategies that you have made. Pinal: But now I’m confused. First you said indexes are good, and then you said that indexes can degrade your performance.  So what is this course about?  I mean how does this course really make an impact? Vinod: Ok -so from the course perspective, what we are trying to do is give you a capsule which gives you a good start. Every journey needs a beginning, you need that first step.  This course is that first step in understanding. This is the most basic, fundamental course that we have tried to attack. This is the fundamentals of indexing, some of the key things that you must know about indexing.   Some of the basics of indexing are lesser known and so I think this course is geared towards each and every one of you out there who wants to understand little bit more about indexing. Pinal: So what I understand is that if I enrolled in this course I will have a minimum understanding about indexing when dealing with performance tuning.  Right? Vinod: Exactly. In this course is we have tried to give you a nice summary. We are talking about clustered indexing, non clustered indexing, too many indexes, too few indexes, over indexing, under indexing, duplicate indexing, columns tune indexing, with SQL Server 2012. There’s lot’s to learn. Pinal: You can see the URL [http://bit.ly/sql-index] of the course on the screen. Go ahead, attend, and let us know what you think about it. Thank you. Vinod: Thank you. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology, Video

    Read the article

  • SQLAuthority News – SQL Server Technical Article – The Data Loading Performance Guide

    - by pinaldave
    The white paper describes load strategies for achieving high-speed data modifications of a Microsoft SQL Server database. “Bulk Load Methods” and “Other Minimally Logged and Metadata Operations” provide an overview of two key and interrelated concepts for high-speed data loading: bulk loading and metadata operations. After this background knowledge, white paper describe how these methods can be [...]

    Read the article

< Previous Page | 8 9 10 11 12 13 14 15 16 17 18 19  | Next Page >